The invention relates generally to digital images and more specifically to segmentation of objects in the digital images to extract content from the images.
Segmenting images of complex, three-dimensional materials into discrete and identifiable objects or targets for analysis is a challenging problem because of the high degree of variability associated with the materials, and inconsistencies between, and anomalies introduced by, the imaging systems themselves.
For example, segmenting or delineating images of biological tissue samples into its constituent parts, such as cells and cellular nuclei, poses a particular significant problem due to additionally introduced variability associated with in staining of the biological material and fluorescence-based microscopy imaging. The three dimensional nature of thin tissue sections introduces out of focus artifacts in magnifications greater than 10×. As an example, the quantification of proteins expression at sub-cellular level is an imperative step in the image analysis process for the quantification of protein expressions of tissue samples. This type of quantitative analysis enables biologists and pathologists to analyze, with a high level of detail, a molecular map of thousands of cells within a given cancer tumor. It also provides new insights into the complex pathways of protein expressions. With the advent of automated image acquisition platforms, such as General Electric's InCell 2000 analyzer, there is an increased need for high content image analysis in the form of automated methods for extracting and analyzing such content from tissue samples.
With regard specifically to biological sample analysis, there are numerous problems associated with detecting and delineating cell nuclei. Cells are three-dimensional objects, and the images of such cells capture a two-dimensional projection that corresponds to the given slice of the tissue. Partial cell volumes that are outside the focal plane are commonly observed. Nuclei shape and size also vary widely across different tissue types and even within the same tissue type. For example, the shape of epithelial cell nuclei in lung tissue is different than the shape of stromal cell nuclei in lung tissue. The grade of a given cancer also may significantly affect the shape and the size of the nuclei. For example, the size of the cell nuclei in breast cancer is a diagnostic indicator.
In addition to cellular variations, staining quality and tissue processing also vary from sample to sample; although non-specific binding and tissue autofluorescence can be reduced, they typically cannot be eliminated; the image acquisition system further introduces noise, particularly, for example, if the image acquisition camera is not actively cooled; and most microscopes are manufactured with tolerances up to 20% non-uniformity of illumination.